INFO 5001 - Computing for Information Science

Fall. 4 credits. Student option grading.

B. Soltoff.

This is an applied course for data scientists with little-to-no programming experience who wish to harness growing digital and computational resources. The focus of the course is on generating reproducible research using programming languages and version control software. Major emphasis is placed on a pragmatic understanding of core principles of programming and packaged implementations of methods. Students will leave the course with basic computational skills implemented through many computational methods and approaches to data science; while students will not become expert programmers, they will gain the knowledge of how to adapt and expand these skills as they are presented with new questions, methods, and data.

Outcome 1: Construct and execute basic programs using elementary programming techniques (e.g. loops, conditional statements, user-defined functions

Outcome 2: Implement data science workflows using common, reproducible methods and software tools.

Outcome 3: Implement statistical learning and machine learning algorithms for a range of data structures.